Traditionally, in a multi-user multi-input multi-output (MU-MIMO) system, particularly in a closed-loop MIMO system, fast feedback of a precoding matrix index (PMI) is generally required, such that a sending end finds its own precoding vector for weighting in a codebook based on the PMI.
However, the performance of the MU-MIMO system cannot be ideally exploited only using the PMI feedback. Experiments already demonstrate that correlation matrix aided MU-MIMO systems with PMI feedback outperform an MU-MIMO system with only PMI feedback.
A quantization process may be utilized in a correlation matrix aided downlink MU-MIMO system, wherein a base station (BS) requires a correlation matrix of downlink sub-channels (to a user equipment UE). In a frequency division duplexing (FDD) digital system, it is necessary to quantize a correlation matrix so as to feed back the correlation matrix from a UE to a BS. Both overhead and precision should be considered to design a desired quantization scheme on correlation matrix feedback. The higher is the precision, the greater is the system overhead; but the cost is increase of the system complexity.
In the IEEE 802.16m specification, a correlation matrix quantization approach has been proposed for a 4×4 antenna array, wherein the quantization approach adopts 28 bits for quantization. However, the current correlation matrix quantization approach in the IEEE 802.16m does not sufficiently exploit the potential and performance of the correlation matrix.